PROJECT SUMMARY/ABSTRACT The United States is in the midst of an opioid epidemic, leading to unprecedented levels of overdose deaths and other harms. Effective treatment for opioid use disorders is available, in the form of opioid agonist therapy (OAT) with methadone or buprenorphine. However, there are significant questions about the risk of adverse clinical outcomes, including mortality and hospitalization during and after treatment, and unplanned treatment cessation. What is the magnitude of these risks, and what patient, treatment setting, and provider factors may contribute to or protect against risk? Additionally, it is increasingly clear that more sophisticated approaches to patient assessment and treatment planning than are currently used are needed to minimise risk. We aim to: 1. Determine the magnitude of risk for specific adverse clinical outcomes (e.g. mortality, hospitalization and ED presentation, and unplanned treatment cessation) during and after OAT with methadone and buprenorphine; 2. Identify patient, treatment setting, and provider risk factors associated with adverse clinical outcomes during and after OAT with methadone and buprenorphine; and 3. Develop a risk prediction model to identify patients at greatest risk of adverse clinical outcomes during and after OAT. To achieve these aims, this project will use existing population-based Australian data on OAT, linked to several health and criminal justice datasets to provide a rich understanding of treatment exposures and outcomes. These data will be used to inform strategies to guide the delivery of high-quality treatment for opioid use disorder in the United States. Specifically, the project will provide data about the magnitude of risk of adverse clinical outcomes during specific treatment and post-treatment periods, and identify patient, treatment setting and provider factors that influence risk. Additionally, it will use innovative machine learning techniques to demonstrate the potential for routinely collected data to be used to assess patient risk at point-of-care, allowing for the development of tailored treatment plans that minimize risk and maximize treatment retention.